Advanced Analytics

Retention matters. Retain the right customers using advanced data analytics to identify indicators of potential fraud and help manage this fraud more effectively

Using advanced analytics to retain the right insurance customersWith organic growth central to many brokers’ and insurers’ strategies, retaining the right customers – those who are profitable and have the highest potential lifetime value – is critical. The impact of fraudulent activity, from a reputation and profitability standpoint, can have serious ramifications on your organization’s ability to retain these customers. Using advanced data analytics to identify indicators of potential fraud and help you manage fraud more effectively could become a key differentiator in this increasingly competitive landscape.

The Insurance Information Institute estimates that property and casualty fraud accounts for $30 billion in annual losses and has a significant impact on both the bottom line and on insurance rates as a whole. Various types of fraud—including opportunistic fraud (usually high volume, low dollar value) and organized fraud (typically low volume but high dollar value)—are giving the insurance industry a bad reputation.

Managing fraud: Move from retroactive to proactiveAs insurers focus in on fraud management as a key part of their strategy, evolving technologies have emerged, providing new opportunities for them to resolve fraudulent claims more effectively. In the past, insurers used a retroactive approach to resolve fraud; that is, they discovered fraudulent claims after they had been paid, and then attempted to collect on those claims after the fact. More recently, insurers are proactive in addressing fraudulent claims by using advanced analytics. This process includes diagnosing the problem using root cause analysis to quickly find patterns in your underlying data and turning those patterns into rules and models—meaningful business insights and rules that you can then turn into concrete strategies and tactics. This allows insurers to quickly identify and pull an “at-risk” claim out of the adjudication process and reassign it to a particular individual within the company for appropriate handling.

According to 126 property and casualty insurers that responded to a survey conducted by SMA Data, published in Insurance Networking News (May 2013), the average insurer invests only nine percent of its IT budget on data and analytics. Compared to the amount of data insurers possess, this is a very low figure. Many are reluctant to take advantage of the benefits of analyzing data—including big data—because they are already plagued with disparate systems requiring a significant amount of “tribal” knowledge and perceive the effort to be “too big.” Indeed, tackling data governance can be a significant effort, but once an insurer plants the seeds, the benefit can be compelling. There are various options for employing advanced analytics to manage and reduce fraudulent claims—including text and pattern analysis and time series and path analysis.

Is your organization able to prevent fraudulent claims from emerging? If you could, how would that impact your customer retention strategies and your bottom line?